Deep Learning for Video Classification: A Review
Open accessAbstract
Video classification task has gained a significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition to the importance of video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are a number of existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers are either outdated, and therefore, do not include the recent state-of-art works or they have some limitations. In order to provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the data sets used. To make the review self- contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided, and the critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.
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After training, the trained model is deployed on the edge or in a central cloud for VSS.
“Afterward, the trained model is deployed on the edge or in a central cloud for VSS [48].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [48]
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Video Action Classification through Graph Convolutional Networks
Costa, F. A., Saito, P. T. M., & Bugatti, P. H. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010321304900497 · Cited by 4 · see citing works
Learning Discriminative Feature with CRF for Unsupervised Video Object SegmentationPreprint
Zhen, M., Li, S., Zhou, L., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01270 · Cited by 3 · see citing works
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
A Novel Hybrid Deep Learning Architecture for Dynamic Hand Gesture Recognition
Hax, D. R. T., Penava, P., Krodel, S., Razova, L., & Buettner, R. (2024) · IEEE Access · doi:10.1109/access.2024.3365274 · Cited by 61 · see citing works
Temporal augmented contrastive learning for micro-expression recognition
Wang, T., & Shang, L. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.02.003 · Cited by 46 · see citing works
A hybrid feature fusion deep learning framework for multi-source medical image analysis
Cao, Q., & Cheng, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2024.103934 · Cited by 34 · see citing works
Benchmarking Edge AI Platforms for High-Performance ML Inference
Jayanth, R., Gupta, N., & Prasanna, V. K. (2024) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec62836.2024.10938499 · Cited by 31 · see citing works
Ramesh, M., & Mahesh, K. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3218431 · Cited by 21 · see citing works
Flow rate quantification of small methane leaks using laser spectroscopy and deep learning
Bergau, M., Strahl, T., Ludlum, K., Scherer, B., & Wöllenstein, J. (2023) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2023.11.059 · Cited by 15 · see citing works
Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging
Kim, M., Pelivanov, I., & O’Donnell, M. (2023) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2023.3329119 · Cited by 12 · see citing works
EEG classification with limited data: A deep clustering approach
Tabejamaat, M., Mohammadzade, H., Negin, F., & Brémond, F. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110934 · Cited by 10 · see citing works
Sequence models for drone versus bird classification
Akyön, F. Ç., Akagündüz, E., Altinuc, S. O., & Temizel, A. (2024) · International Conference on Machine Vision · doi:10.1117/12.3023457 · Cited by 6 · see citing works
Cherif, L., Safdar, M., Lamouche, G., Wanjara, P., Paul, P. P., Wood, G., Zimmermann, M., Hannesen, F., & Zhao, Y. F. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.1235 · Cited by 4 · see citing works
Angle Diagnosis of Blast Furnace Chute Through Deep Learning of Temporal Images
He, Q., Liang, H., Yan, B., & Guo, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3374315 · Cited by 4 · see citing works
Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification
Awan, M., Nadeem, A., Awan, M. J., Mustafa, A., & Husain, S. S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93806-1_15 · Cited by 4 · see citing works
An Efficient Algorithm for Fake Video Detection
Nibras, N., Fahim, S., Sakib, S., Rashid, S. U., & Rahman, A. (2024) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict62016.2024.10534423 · Cited by 1 · see citing works
Video Classification-Based Action Recognition with Enhanced Convolutional Neural Networks
Mei, B. (2023) · International Conference on Advances in Computing and Information Technologies (ACITY) · doi:10.5121/csit.2023.131506 · Cited by 1 · see citing works
Crowd Violence Detection Using Deep Learning Techniques and Explanation Using XaiPreprint
Gite, S., Tiwari, C., Chandana, J., Chanumolu, S. V., Shrivastava, A., & Kotecha, K. V. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4524940 · Cited by 1 · see citing works
Spatiotemporal Modeling for Dynamic Gesture Recognition in Video StreamsPreprint
Sandhya, J., & ANITHASHEELA, K. (2024) · Research Square · doi:10.21203/rs.3.rs-4019650/v1 · Cited by 1 · see citing works
- Authors
- Rehman, A., & Belhaouari, S. B.
- Published
- 2021-08-16
- OpenAlex
- W4254816946
- Cited by
- 17
- References
- 93
- Citations by year
- 2025: 1 · 2024: 8 · 2023: 7 · 2022: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- video classification, deep learning architectures, video recognition, benchmark dataset, performance indicators, future research directions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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